The DOI matches its Crossref record, with a small difference in title or year; 31 of the 31 references it cites were found in a registry or an index; 2 of the 14 sources its claims cite were checked for support.
ImageNet classification with deep convolutional neural networks
Open accessAbstract
We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully connected layers we employed a recently developed regularization method called "dropout" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry.
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Claims the paper makes
29 No basis given · 22 Own result · 19 Own interpretation · 4 No full text of the sources · 3 Sources not found · 2 Supported · 2 Not checked yet · 1 Own method
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In the paper
“This technique reduces complex co adaptations of neurons, since a neuron cannot rely on the presence of particular other neurons.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.2. Dropout
What the sources say
- Supportedcites [12] 12. Hinton, G., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 (2012). source
The paper states on p. 1 that dropout prevents complex co-adaptations by randomly omitting hidden units, so a hidden unit cannot rely on other hidden units being present. This directly supports the citing sentence. (LLM, conf=0.95)
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In the paper
“There is, however, a very efficient version of model combination that only costs about a factor of two during training.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.2. Dropout
What the sources say
- Supportedcites [12] 12. Hinton, G., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 (2012). source
The paper describes dropout as an efficient way to combine many neural-network models (p. 1). In F.6 Training (p. 17), it reports that ImageNet training took roughly four days with dropout versus two days without it, supporting the approximate factor-of-two training cost. (LLM, conf=0.88)
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In the paper
“This scheme reduces our top-1 and top-5 error rates by 1.7% and 1.2%, respectively, as compared with a net with half as many kernels in each convolutional layer trained on one GPU.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 4.2. Training on multiple GPUs
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In the paper
“The size of our network made overfitting a significant problem, even with 1.2 million labeled training examples”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
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In the paper
“So these data augmentation schemes are, in effect, computationally free.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.1. Data augmentation
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In the paper
“we found that removing any convolutional layer (each of which contains no more than 1% of the model's parameters) resulted in inferior performance.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
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In the paper
“This scheme reduces the top-1 and top-5 error rates by 0.4% and 0.3%, respectively, as compared with the non overlapping scheme s = 2, z = 2, which produces output of equivalent dimensions.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.1. Data augmentation
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In the paper
“This increases the size of our training set by a factor of 2048”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.1. Data augmentation
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In the paper
“Deep CNNs with ReLUs train several times faster than their equivalents with tanh units.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 4.1. Rectified Linear Unit nonlinearity
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In the paper
“However, we still find that the following local normalization scheme aids generalization.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 4.3. Local response normalization
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In the paper
“Although the 1000 classes of ILSVRC make each training example impose 10 bits of constraint on the mapping from image to label, this turns out to be insufficient to learn so many parameters without considerable overfitting.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5. REDUCING OVERFITTING
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In the paper
“though the resulting training examples are, of course, highly inter dependent.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.1. Data augmentation
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In the paper
“On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively”
§ ImageNet Classification with Deep Convolutional Neural Networks > Abstract
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In the paper
“We generally observe during training that models with overlapping pooling find it slightly more difficult to overfit.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.1. Data augmentation
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In the paper
“a four-layer CNN achieved a 13% test e r r o r r a t e w i t h o u t n o r m a l i z a t i o n a n d 1 1 % w i t h normalization.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 4.3. Local response normalization
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In the paper
“so we used several effective techniques for preventing overfitting”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
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In the paper
“Response normalization reduces our top-1 and top-5 error rates by 1.4% and 1.2%, respectively.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 4.3. Local response normalization
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In the paper
“The two-GPU net takes slightly less time to train than the one-GPU net.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 4.2. Training on multiple GPUs
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In the paper
“Our network contains a number of new and unusual features which improve its performance and reduce its training time”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
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In the paper
“Without this scheme, our network suffers from substantial overfitting, which would have forced us to use much smaller networks.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.1. Data augmentation
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In the paper
“For example, only other types of cat are considered plausible labels for the leopard.”
§ ImageNet Classification with Deep Convolutional Neural Networks > Model
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In the paper
“This initialization accelerates the early stages of learning by providing the ReLUs with positive inputs.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 6. DETAILS OF LEARNING
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In the paper
“Notice that at the pixel level, the retrieved training images are generally not close in L2 to the query images in the first column.”
§ ImageNet Classification with Deep Convolutional Neural Networks > Model
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In the paper
“Training one CNN, with an extra sixth convolutional layer over the last pooling layer, to classify the entire ImageNet Fall 2011 release (15M images, 22K categories), and then”
§ ImageNet Classification with Deep Convolutional Neural Networks > Model
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Our top-1 and top-5 error rates on this dataset are 67.4% and 40.9%, attained by the net described above but with an additional, sixth convolutional layer over the last pooling layer.”
§ ImageNet Classification with Deep Convolutional Neural Networks > Model
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- Rests on an assumption: The Fall 2009 ImageNet split used by the authors, which differs from previous authors' splits, does not affect the results appreciably.
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In the paper
“This kind of specialization occurs during every run and is independent of any particular random weight initialization (modulo a renumbering of the GPUs).”
§ ImageNet Classification with Deep Convolutional Neural Networks > Model
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In the paper
“The magnitude of the effect demonstrated here varies with network architecture, but networks with ReLUs consistently learn several times faster than equivalents with saturating neurons.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 9. EPILOGUE
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In the paper
“Our network achieves top-1 and top-5 test set error rates of 37.5% and 17.0%, respectively.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 7. RESULTS
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In the paper
“Averaging the predictions of five similar CNNs gives an error rate of 16.4%.”
§ ImageNet Classification with Deep Convolutional Neural Networks > Model
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In the paper
“In the remainder of this paragraph, we use validation and test error rates interchangeably because in our experience they do not differ by more than 0.1% (see Table 2).”
§ ImageNet Classification with Deep Convolutional Neural Networks > Model
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In the paper
“Notice that even off-center objects, such as the mite in the top-left, can be recognized by the net.”
§ ImageNet Classification with Deep Convolutional Neural Networks > Model
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In the paper
“All of our experiments suggest that our results can be improved simply by waiting for faster GPUs and bigger datasets to become available.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
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In the paper
“ReLUs have the desirable property that they do not require input normalization to prevent them from saturating.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 4.3. Local response normalization
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None of the sources cited for this claim have been checked yet.
In the paper
“Convolutional neural networks (CNNs) constitute one such class of models.9,15,17,19,21,26,32”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
What the sources say
- Not checked — no full text of the sourcecites [9] 9. Fukushima, K. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern. 36, 4 (1980), 193-202. source
- Not checked yetcites [15] 15. Krizhevsky, A. Convolutional deep belief networks on cifar-10. Unpublished manuscript, 2010.
- Not checked yetcites [17] 17. LeCun, Y., Boser, B., Denker, J., Henderson, D., Howard, R., Hubbard, W., Jackel, L., et al. Handwritten digit recognition with a back-propagation network. In Advances in Neural Information Processing Systems (1990).
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In the paper
“However, the immense complexity of the object recognition task means that this problem cannot be specified even by a dataset as large as ImageNet, so our model should also have lots of prior knowledge to compensate for all the data we do not have.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Thus, compared to standard feedforward neural networks with similarly sized layers, CNNs have much fewer connections and parameters and so they are easier to train, while their theoretically best performance is likely to be only slightly worse.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
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In the paper
“methods that require careful hand-engineering by a programmer who understands the domain do not scale as well as methods that replace the programmer with a powerful general-purpose learning procedure.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 1. PROLOGUE
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In the paper
“Luckily, current GPUs, paired with a highly optimized implementation of 2D convolution, are powerful enough to facilitate the training of interestinglylarge CNNs”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
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In the paper
“they also make strong and mostly correct assumptions about the nature of images (namely, stationarity of statistics and locality of pixel dependencies).”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
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In the paper
“Despite the attractive qualities of CNNs, and despite the relative efficiency of their local architecture, they have still been prohibitively expensive to apply in large scale to highresolution images.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
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In the paper
“ImageNet, 7 which consists of over 15 million labeled high-resolution images in over 22,000 categories.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
What the sources say
- Not checked — no full text of the sourcecites [7] 7. Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L. ImageNet: A large-scale hierarchical image database. In CVPR09 (2009). source
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In the paper
“which is considerably better than the previous state-of-the-art”
§ ImageNet Classification with Deep Convolutional Neural Networks > Abstract
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In the paper
“With enough computation and enough data, learning beats programming for complicated tasks that require the integration of many different, noisy cues.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 1. PROLOGUE
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Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“and recent datasets such as ImageNet contain enough labeled examples to train such models without severe overfitting.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“for deep neural networks to shine, they needed far more labeled data and hugely more computation.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 1. PROLOGUE
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If at least some training examples produce a positive input to a ReLU, learning will happen in that neuron.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 4.3. Local response normalization
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“To learn about thousands of objects from millions of images, we need a model with a large learning capacity.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The constants k, n, α, and β are hyperparameters whose values are determined using a validation set”
§ ImageNet Classification with Deep Convolutional Neural Networks > 4.3. Local response normalization
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, on this dataset the primary concern is preventing overfitting, so the effect they are observing is different from the accelerated ability to fit the training set which we report when using ReLUs.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 4.1. Rectified Linear Unit nonlinearity
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“since then the best published results are 45.7% and 25.7% with an approach that averages the predictions of two classifiers trained on Fisher Vectors (FVs) computed from two types of densely sampled features.29”
§ ImageNet Classification with Deep Convolutional Neural Networks > 7. RESULTS
What the sources say
- Not checked — no full text of the sourcecites [29] 29. Sánchez, J., Perronnin, F. High- dimensional signature compression for large-scale image classification. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011 (2011). IEEE, 1665-1672. source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“Combining the predictions of many different models is a very successful way to reduce test errors, 1, 3”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.2. Dropout
What the sources say
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“but it appears to be too expensive for big neural networks that already take several days to train.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.2. Dropout
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“The best published results on this dataset are 78.1% and 60.9%.23”
§ ImageNet Classification with Deep Convolutional Neural Networks > Model
What the sources say
- Not checked — no full text of the sourcecites [23] 23. Mensink, T., Verbeek, J., Perronnin, F., Csurka, G. Metric learning for large scale image classification: Generalizing to new classes at near-zero cost. In ECCV -European Conference on Computer Vision (Florence, Italy, Oct. 2012). source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper relies on this without support.
Objects in realistic settings exhibit considerable variability.
“But objects in realistic settings exhibit considerable variability”
The paper relies on this without support.
Object identity is invariant to changes in the intensity and color of illumination.
“object identity is invariant to changes in the intensity and color of the illumination.”
The paper relies on this without support.
The Fall 2009 ImageNet split used by the authors, which differs from previous authors' splits, does not affect the results appreciably.
“Since there is no established test set, our split necessarily differs from the splits used by previous authors, but this does not affect the results appreciably.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In the field of DL, the CNN is the most famous and commonly employed algorithm [30, 71–75].”
From Review of deep learning: concepts, CNN architectures, challenges, applications, future directions (2021) · cites this paper as [30]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Fully convolutional neural networks (fCNNs) have provided state-of-the-art performance for object classification and image segmentation (1).”
From Fully automated, real-time 3D ultrasound segmentation to estimate first trimester placental volume using deep learning (2018) · cites this paper as [1]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The first wave saw the emergence of deep learning as the dominant paradigm for analyzing high-dimensional biological data.”
From The next paradigm in bioinformatics: a review of multi-agent systems and foundational models for end-to-end scientific discovery (2026) · cites this paper as [1]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Krizhevesky et al. [30] first proposed AlexNet and consequently improved the CNN learning ability by increasing its depth and implementing several parameter optimization strategies.”
From Review of deep learning: concepts, CNN architectures, challenges, applications, future directions (2021) · cites this paper as [30]
The cited source says what the claim attributes to it.
In the citing paper
“In deep CNN architecture, AlexNet is highly respected [30], as it achieved innovative results in the fields of image recognition and classification.”
From Review of deep learning: concepts, CNN architectures, challenges, applications, future directions (2021) · cites this paper as [30]
The cited source says what the claim attributes to it.
In the citing paper
“Many CNN models, e.g. AlexNet [30], GoogleNet [103], and ResNet [37], have been trained on large datasets such as ImageNet for image recognition purposes.”
From Review of deep learning: concepts, CNN architectures, challenges, applications, future directions (2021) · cites this paper as [30]
The cited source says what the claim attributes to it.
In the citing paper
“To avoid model-specific biases, two different AI models, AlexNet [22] and VGG16 [38], were selected for classification.”
From Study on the Helpfulness of Explainable Artificial Intelligence (2024) · cites this paper as [22]
The cited source says what the claim attributes to it.
In the citing paper
“In particular, the choice of this specific size is based on the fact that the AlexNet 34 (and also similar CNN architectures) has been shown to offer satisfactory performances with an input matrix of that size or very similar, albeit for different types of problems with respect to the one presented in this work (e.g., image classification).”
The cited source says what the claim attributes to it.
In the citing paper
“Jung et al. [21] found that a reduction in accuracy does not occur when using only four bits for both weight and activation parameters, even for the ImageNet dataset [32].”
From Zero-Centered Fixed-Point Quantization With Iterative Retraining for Deep Convolutional Neural Network-Based Object Detectors (2021) · cites this paper as [32]
The cited source says what the claim attributes to it.
In the citing paper
“Feature-based approaches [17,18,19] commonly employ networks pre-trained on ImageNet [20] to map original images into a more distinguishable feature space. These methods are typically characterized by low training costs and outstanding performance.”
From An Unsupervised Method for Industrial Image Anomaly Detection with Vision Transformer-Based Autoencoder (2024) · cites this paper as [20]
The cited source says what the claim attributes to it.
In the citing paper
“Over the past several years, the successes of deep learning on visual tasks has critically relied on convolutional neural networks [20, 16].”
From Do Vision Transformers See Like Convolutional Neural Networks? (2021) · cites this paper as [20]
The cited source says what the claim attributes to it.
In the citing paper
“A common perception metric is the Learned Perceptual Image Patch Similarity (LPIPS) which uses pre-trained deep neural network backbones such as VGG [9] and AlexNet [22].”
From MTReD: 3D Reconstruction Dataset for Fly-over Videos of Maritime Domain (2025) · cites this paper as [22]
The cited source says what the claim attributes to it.
In the citing paper
“Starting with the the success of AlexNet [5] in the ILSVRC challenge [6] in 2012, deep learning models have been outperforming traditional approaches on a number of computer vision problems.”
From Satellite Pose Estimation Challenge: Dataset, Competition Design, and Results (2020) · cites this paper as [5]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Unfortunately, it has been acknowledged that the performance of those hashing schemes may degrade significantly if the projected dimensions are imbalanced [4], [6], [13], i.e., their variances vary a lot.”
From Unsupervised Deep Video Hashing via Balanced Code for Large-Scale Video Retrieval (2019) · cites this paper as [6]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“When convolutional RBMs are stacked together, they form convolutional deep belief networks [22].”
From Multimodal emotion recognition using deep learning architectures (2016) · cites this paper as [22]
Experimental — accuracy not yet measuredIn the citing paper
“Convolutional neural networks (CNNs), built for spatial data, have demonstrated performance comparable to that of specialists in cancer detection and tumor segmentation [15,16,17,18,19,20].”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [16]
In the citing paper
“When a low-capacity model, such as AlexNet, is used for pre-training, little improvement can be obtained when compared to the Resnet50 family [26].”
From Self-supervised learning: A succinct review (2023) · cites this paper as [26]
In the citing paper
“Since its introduction by Krizhevsky et al. [7] in 2012, the CNN model has been successfully applied to a wide range of multimodal applications, such as image dehazing [239, 240] and human activity recognition [241].”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [7]
In the citing paper
“The AlexNet architecture was published in [4], achieved significantly improved performance over the other non-deep learning methods for ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2012.”
From Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning (2016) · cites this paper as [4]
In the citing paper
“Deep CNN architectures with 8, even 22 layers [4], [33], can be useful even for CADe problems where the available training datasets are limited.”
From Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning (2016) · cites this paper as [4]
In the citing paper
“focusing on AI and deep learning architectures that have already become the de facto approach.”
From AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020) · cites this paper as [101]
In the citing paper
“Since then, fueled by the revolutionary results of AlexNet [101] and incarnations of patch-based adaptations of Deep Boltzmann Machines and stacked autoencoders, deep learning based segmentation of anatomy and pathology has witnessed a revolution (see also Table II), where for some tasks now we observe human level performance [102].”
From AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020) · cites this paper as [101]
In the citing paper
“ReLU significantly increases the rate of con-vergence of stochastic gradient descent in comparison with the sigmoid [26].”
From Real-time system for automatic cold strip surface defect detection (2019) · cites this paper as [26]
In the citing paper
“Real-time volume estimation has been achieved using fully convolutional neural networks (FCNNs) [11] to produce state-of-the-art performance in a range of medical imaging modalities [12].”
From Fully Automated 3-D Ultrasound Segmentation of the Placenta, Amniotic Fluid, and Fetus for Early Pregnancy Assessment (2021) · cites this paper as [11]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
“we found that removing any convolutional layer (each of which contains no more than 1% of the model's parameters) resulted in inferior performance.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 4.5. Overall architecture
“The third convolutional layer has 384 kernels of size 3 × 3 × 256”
Citations
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Bhadauria, A. S., & Verma, V. S. (2025) · Global AI Summit - International Conference on Artificial Intelligence and Emerging Technology (AI Summit) · doi:10.1109/aisummit66170.2025.11411094
RecSys-Fairness: A Framework for Reducing Group Unfairness in Recommendations
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“Security risks for network systems can arise when IDS systems incorrectly identify malicious traffic as usual because of adversarial examples [19].”
Li, Q.-A., Wang, B., Zhang, P., et al. (2026) · International Journal of Remote Sensing · doi:10.1080/01431161.2026.2633778 · Cited by 1
“…farmland identification primarily employs two paradigms: conventional methods based on the ‘feature engineering + classifier’ framework through manual feature design (Han et al. 2025; Krizhevsky, Sutskever, and Hinton 2017; P. Zhang et al. 2023a), and emerging deep learning solutions.”
Research on Image Classification Based on Deep Learning Technologies
Zhang, G. (2025) · International Conference on Electronic Information Engineering and Computer Communication (EIECC) · doi:10.1109/eiecc67963.2025.11409633
“In 2012, AlexNet achieved a Top-5 error rate of 15.3% in the ImageNet competition [5] , heralding a new era in data-driven image classification.”
InfluentialPang, Z., Zhu, W., Zhang, R., Liu, X., Huang, Q., Wu, J., Yang, T., Shang, H., & Wei, Z. (2026) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2026.3671121
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“The rapid development of Internet of Things (IoT), edge intelligence, and real-time perception applications has driven the migration of smart sensing tasks such as image classification and object detection [1] [2], from the cloud to near-sensor edge devices.”
Okumura, K., & Sakai, H. (2026) · AQUA - Water Infrastructure Ecosystems and Society · doi:10.2166/aqua.2026.110
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“Early approaches predominantly utilized straightforward feature matching techniques [16] for semantic alignment, largely overlooking the deeper semantic relations between textual queries and video content.”
Kushwah, S. S., Punn, N. S., & Bhattacharya, M. (2026) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3800952 · Cited by 1
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“This architecture, introduced by Alex Krizhevskii et al. in 2012 [38,60,61], achieved significant success in the ImageNet Large Scale Visual Recognition Challenge of 2012, an effort that evaluates algorithmic performance in object detection and image classification.”
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“AlexNet [27] is renowned for its deep convolutional architecture, comprising eight layers, including five convolutional layers and three fully connected layers.”
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Tyagi, N., Rai, A. K., & Singh, T. (2025) · International Conference on IoT, Communication and Automation Technology (ICICAT) · doi:10.1109/icicat68430.2025.11414643
Shen, Y., Liu, W., & Zhang, Y. (2026) · Multimedia Systems · doi:10.1007/s00530-026-02244-7
Umeike, R., Dao, T., Crawford, S., et al. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6381650
Diagnosis of Wiring Networks Using Reflectometry and the Inception-ResNet-v2 Network
Goudjil, A., Smail, M. K., Bouchekara, H. R. E. H., & Boubezoul, A. (2025) · International Conference on the Computation of Electromagnetic Fields (COMPUMAG) · doi:10.1109/compumag60611.2025.11421266
Classification of Variable Stars Using Convolutional Neural Network
Bindu, A. P., Olmschenk, G., & Zhu, Z. (2025) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw69685.2025.00036
“Later, in 2012, Krizhevsky et. al [11] proposed AlexNet, the first CNN that outperformed all other existing machine learning frameworks.”
Medicinal Plant Detection and Analysis using Deep Learning on Leaf Images
Gopika., S., BhavadharaniS, BhuvanaGayathri.G, Sreekrishna, M., & Hamsa, C. (2026) · International Conference on Electronics and Renewable Systems (ICEARS) · doi:10.1109/icears67481.2026.11416649
“Studies in recent years have shown that deep CNN architectures like ResNet-50 and InceptionV3 can achieve classification accuracies above 90% for leaf images [6][7].”
Deciphering the field of uplift from landscape: A genetic algorithm‐based inversion method
Zhao, X., Shen, Y., Zhang, G., et al. (2026) · Earth Surface Processes and Landforms · doi:10.1002/esp.70266
“LPIPS, implemented using a pre-trained AlexNet convolutional neural network (Krizhevsky, Sutskever, & Hinton, 2012), is designed to quantify perceptual distance between images, effectively serving as a proxy for human visual judgment.”
Thermally controllable diffractive processor using hybrid metasurfacesPreprint
Zang, X., Wang, G., Huang, X., Zhang, T., Tan, Z., Wu, J., Jin, B. B., Zhu, Y., & Zhuang, S. (2026) · Research Square · doi:10.21203/rs.3.rs-8811837/v1
Roosan, D., Hai, F., Nirzhor, S., et al. (2026) · IEEE Mediterranean Electrotechnical Conference (MELECON) · doi:10.1109/melecon64486.2026.11418875
“Meanwhile, real-time pill identification involves running convolutional neural networks or other image recognition algorithms on streaming camera data, which is also computationally demanding [21].”
Newegy, S., & Zhang, B. Y. (2026) · IEEE Mediterranean Electrotechnical Conference (MELECON) · doi:10.1109/melecon64486.2026.11418822
“The network comprises two convolutional layers (32 and 64 filters) for spatial features [22], followed by an LSTM (100 units) [23] to capture temporal dependencies across 10-frame sequences.”
Fauzi, A., Arif, T. Y., Away, Y., & Roslidar, R. (2025) · International Conference on Computer System, Information Technology, and Electrical Engineering (COSITE) · doi:10.1109/cosite68330.2025.11414257
“CNNs operate by recognizing local patterns within images using kernels or filters, and they offer advantages in parameter reduction compared to fully connected networks [15], [16], .”
Wang, M., Jia, F., Yang, C., et al. (2026) · Intelligent Computing · doi:10.34133/icomputing.0321
Deep Learning-Based Detection of Unauthorized Buildings Using Aerial Images: A Comparative Study
Son, H., & Kim, D. H. (2025) · Journal of the Korean society for quality management · doi:10.7469/jksqm.2025.53.3.315 · Cited by 1
“딥 러닝은 여러 계층(layer)을 통해 초기 입력으로부터 점진적으로 더 복잡한 수준의 특성을 추출하는 머신러닝 알고리즘의 일종이다(Deng et al., 2014 D. et al., 2012; Krizhevsky et al., 2012).”
Autonomous discovery of hidden physical structures via competitive physics-informed neural expertsPreprint
Peng, M., & Tang, H. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6403685
Shekh, M. J., Parson, C., Mishra, A., Sanyal, S., & Vora, H. D. (2026) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-026-17804-4
Liu, B., Lyu, Q., Wang, X., Dong, J., Liu, L., & Han, Z. J. (2026) · IEEE Transactions on Image Processing · doi:10.1109/tip.2026.3671625
“Prompting for Continual Video Instance Segmentation Baichen Liu , Qi Lyu , Xudong Wang , Jiahua Dong , Lianqing Liu , Senior Member, IEEE , and Zhi Han , Member, IEEE Abstract —Continual video instance segmentation (CVIS) requires the plasticity to absorb new categories while maintaining the…”
Bahram, A. M., Bahram, A. M., Omer, S. M., & Mohammed, H. M. (2026) · Charmo Journal of Natural Sciences and Technologies · doi:10.31530/cjnst.2026.2.1.2 · Cited by 2
Wang, M., Shangguan, H., Yang, Y., et al. (2026) · Journal of Advanced Research · doi:10.1016/j.jare.2026.03.009
Du, S., Ma, G., Hwang, H., et al. (2026) · Advances in Structural Engineering · doi:10.1177/13694332261430946
Thomas, A., Aravind, V., Naik, B. C., et al. (2026) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-026-02819-1
Liang, A., Zang, X., He, J., Cui, Q., Xu, C., & Gu, B. (2026) · Optics Communications · doi:10.1016/j.optcom.2026.133116
Overdispersion Parameter Estimation For Negative Binomial Optimization For Low-Photon Imaging
Lu, Y., & Marcia, R. F. (2025) · IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing · doi:10.1109/camsap66162.2025.11423969
New Directions in Digital Pathology
Sun, C. W., Lee, J. B., & Motaparthi, K. (2026) · Dermatologic Clinics · doi:10.1016/j.det.2026.01.012
He, K., Wu, Y., Wu, J., & Guo, Y. (2026) · Journal of Physics B Atomic Molecular and Optical Physics · doi:10.1088/1361-6455/ae509f
“A one-dimensional convolutional neural network (1D-CNN) (Krizhevsky et al 2017, Tang et al 2017) can capture local correlations by matching convolutional kernels and can integrate global patterns using multi-layer convolution and pooling.”
Mohamed, A. M., Khan, M. N., & Ahmed, M. M. (2026) · Electronics · doi:10.3390/electronics15061188
Enhancing Content Representation for AR Image Quality Assessment Using Knowledge Distillation
Sekhri, A., Amirshahi, S. A., & Larabi, M. (2026) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2026.3673101
RF$^{2}$ Transformer: Refocusing Transformer for RF Sensing
Song, R., Lu, Z., Fang, L., Wu, Z., Yang, S., Zhang, D., Hu, Y., Lin, H., & Chen, Y. (2026) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2026.3673427
Human Body Part Image Segmentation Based on Improved K-NET
Dong, Z., Li, K. K., & Li, D. (2025) · International Conference on Computers and Artificial Intelligence Technology (CAIT) · doi:10.1109/cait68620.2025.11424812
“Deep convolutional neural networks(DCNNs) perform strongly in image classification[7]-[11] and object detection[12][13], and many state-of-the-art models have emerged for instance segmentation[14]-[17].”
Huang, W., Fan, R., & Su, Y. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3673750
Maulana, T., Sekti, B. A., Tjahjono, B., & Widayanti, R. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/404018
Lee, J.-O., & Kim, Y. S. (2025) · Journal of the Korean society for quality management · doi:10.7469/jksqm.2025.53.4.523
“연구 초기에는 이미지 분류 성능 향상을 위해 회전, 자르기와 같은 단순한 기하학적 변환 기법이 주로 활용되었으며(Krizhevsky et al., 2012), 이후 Mixup, CutMix, AutoAugment와 같은 보다 정교한 방법론들이 제안되었다.”
da Silva, N. F., Filho, G. P. R., Immich, R., Gonçalves, V. P., & Meneguette, R. I. (2026) · Journal of the Brazilian Computer Society · doi:10.5753/jbcs.2026.5370
Shen, Y. (2026) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001426400045
Dutta, A. K., Aljarallah, N. A., & Sait, A. R. W. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1709872
Shianios, D., & Kyrkou, C. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.133282
He, Y., Xiong, K., Hou, H., et al. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6410735
AI Infrastructures, Total Mobilisation and Decomputing
McQuillan, D. (2026) · Springer eBooks · doi:10.1007/978-3-032-09748-4_14 · Cited by 1
Advances in computer vision for recognizing microstructure images of metallic materials
Dong, X., Xu, P., Bai, B., Jia, L., He, X., & Yang, W. (2026) · Materials Today Communications · doi:10.1016/j.mtcomm.2026.114989
Awasthi, V., Kumar, H., Tiwari, M., et al. (2026) · MethodsX · doi:10.1016/j.mex.2026.103868
Machine learning-based emission rate estimates of global methane super-emissions
Roberts, C., Maasakkers, J. D., de Jong, T. A., et al. (2026) · Atmospheric measurement techniques · doi:10.5194/amt-19-5659-2026
Dong, S., Guo, Y., Wu, Z., et al. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.114484
Ghiffari, M. A., Herdiani, F. D., Ariawan, I., & Rusmawati, D. A. (2026) · Sakarya University Journal of Computer and Information Sciences · doi:10.35377/saucis...1777006 · Cited by 1
Data-driven paradigms for advancing alkali-metal-ion battery technologies
Shao, H., Kumar, N., Lai, W.-H., Li, H., Jia, X., Liu, H.-K., & Wang, Y.-X. (2026) · Energy storage materials · doi:10.1016/j.ensm.2026.105044
Predicting Fruit Quality with a Hybrid Machine Learning and Image Processing Approach
Hashemi, A., & Amiri, S. (2026) · University of Michigan Undergraduate Research Journal · doi:10.3998/umurj.9836
“The CNN model is a deep learning model that is particularly suitable for image recognition tasks (Krizhevsky et al., 2017).”
Fault Diagnosis for Power Inverter Based on Feature Transformation with Imbalanced Samples
Sun, Q., Dong, J., & Zhang, Z. (2025) · Global Reliability and Prognostics and Health Management Conference (PHM) · doi:10.1109/phm-xian66756.2025.11427767
“This modification significantly improves training stability and the fidelity of generated samples [17].”
A dual-branch graph neural network architecture for drug-target binding affinity prediction
Abbas, K., Hao, C., Shi, D., Zhou, W., Tang, Y., Islam, S., Hasan, M. K., Abdullah, S. N. H. S., & Hou, R. (2026) · Scientific Reports · doi:10.1038/s41598-026-43782-4
Algashev, G., & Lezin, I. (2025) · International Conference on Information, Control, and Communication Technologies (ICCT) · doi:10.1109/icct67028.2025.11427793
“Today, they are used in assembly control systems, defect detection, alignment, and robotic production lines where precise part localization is essential [1], [14].”
InfluentialA high-precision spacecraft bearing fault diagnosis method based on improved EMD-CNN-LSTM fusion
Li, J., Du, Y., & Pan, X. (2025) · Global Reliability and Prognostics and Health Management Conference (PHM) · doi:10.1109/phm-xian66756.2025.11427684
Shayid, M. A. (2026) · International Conference on Electrical, Computer & Telecommunication Engineering (ICECTE) · doi:10.1109/icecte69292.2026.11429400
“To avoid overfitting in these models, we commonly apply regularisation approaches like L2 regularisation, dropout, batch normalisation, and data augmentation [4], [5].”
Consistency regularization for complementary clothing recommendations
Liao, S., Mok, P. Y., & Li, L. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.115069
Region-guided preprocessing and U-Net variants for improved panoramic dental radiograph segmentation
Sogur, N., & Kazak, N. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110068
Aligning machines with human values: opacity and ethical sustainability of AI
Cianciaruso, F. M. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01111-8
Bao, Z., Zhang, S., Yang, Y., et al. (2026) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2026.3674867
Fajardo, A. C., Teleron, J. I., Gonzales, S. L., & Horteza, A. (2025) · International Conference on Computer and Applications (ICCA) · doi:10.1109/icca66035.2025.11430841
“Using 1D convolutional layers, the CNN identifies local temporal patterns [24] by sliding filters across the time dimension.”
Decentralized Information Elicitation Without Verification
Chen, K., Huang, C. H., & Huang, J. (2026) · IEEE Transactions on Networking · doi:10.1109/ton.2026.3674784
Accessible parking spot location using satellite images
Arjona, J., Catasús, O., Montero, J., et al. (2026) · Transportation research procedia · doi:10.1016/j.trpro.2026.02.065
Chen, X., Hou, J., Dai, J., et al. (2026) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-026-03029-0
Cai, L., Zhang, Z., Cheng, Z., & Wu, Y. (2025) · IEEE International Conference on Artificial Intelligence, Computer Science and Information Processing (AICSIP) · doi:10.1109/aicsip65423.2025.11427193
“Each convolutional layer is followed by an activation function (ReLU) to introduce non-linearity into the model [37, 38].”
Neural Signal Compression Using RAMAN tinyML Accelerator for BCI Applications
Krishna, A., Debnath, S., Srivatsav, M., André, V. S., Mehendale, M. M., & Thakur, C. S. (2026) · IEEE transactions on circuits and systems for artificial intelligence. · doi:10.1109/tcasai.2026.3674434
Automated quantification of chronic constipation in X-rays using U-Net
Takashima, N., Fujita, D., Sanuki, T., Kinoshita, Y., & Kobashi, S. (2026) · Soft Computing · doi:10.1007/s00500-025-10844-0
“To address this challenge, pre-training strategies are utilized, demonstrating success in fields like natural lan - guage processing (DevLin et al. 2018) and image recognition (Krizhevsky et al. 2017).”
Gu, S., Qiao, G., & Zhu, J. (2026) · Future Foods · doi:10.1016/j.fufo.2026.100990
An interpretable spatial feature selection for dimensionality reduction in image classification
Hwang, J.-H., & Choi, D.-H. (2026) · Pattern Analysis and Applications · doi:10.1007/s10044-026-01654-7
Modern machine learning in thin film device development
Dobrynin, A., Khaydukov, Y., & Gubbins, M. (2026) · Chemical Physics Reviews · doi:10.1063/5.0231166
Machine Learning Methods for Disaster Risk Assessment and Strategic Resource Allocation
Chaudhary, S., Garg, K., Gupta, P., et al. (2025) · doi:10.1109/acet67282.2025.11430246
“Hyper - parameters can be freely modified using a grid search or random search method [8].”
Metadata
- Authors
- Krizhevsky, A., Sutskever, I., & Hinton, G. E.
- Affiliations
- Google (United States) (US)
- University of Toronto (CA)
- Venue
- Communications of the ACM
- Published
- 2017-05-24
- Volume
- 60
- Issue
- 6
- Pages
- 84-90
- ISSN
- 0001-0782 (ISSN-L)
- 1557-7317
- OpenAlex
- W2163605009
- Cited by
- 112,155
- References
- 27
- Citations by year
- 2026: 2710 · 2025: 7710 · 2024: 10211 · 2023: 12267 · 2022: 12280 · 2021: 15497 · 2020: 14538 · 2019: 13976 · 2018: 9999 · 2017: 6661
- FWCI
- 5127.18
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Communications of the ACM (publishedVersion)
- neural information processing systems
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- ImageNet, image classification, deep convolutional neural networks, dropout regularization, overfitting
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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